LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Tian, Yang; Wang, Xuan; Zhu, Shengjie; Xu, Fang; Liu, Jinghong

doi:10.3390/rs15174358

Cited by 5 publications

(3 citation statements)

References 78 publications

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“…Therefore, FPN can more comprehensively detect multi-scale ships. Tian et al [87] and Ren et al [70] proposed a multi-node feature fusion method based on FPN. It fully integrates information from feature maps at different scales, and improves the detection ability of multi-scale ships.…”

Section: Multi-scale Information-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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Ship detection aims to automatically identify whether there are ships in the images, precisely classifies and localizes them. Regardless of whether utilizing early manually designed methods or deep learning technology, ship detection is dedicated to exploring the inherent characteristics of ships to enhance recall. Nowadays, high-precision ship detection plays a crucial role in civilian and military applications. In order to provide a comprehensive review of ship detection in optical remote-sensing images (SDORSIs), this paper summarizes the challenges as a guide. These challenges include complex marine environments, insufficient discriminative features, large scale variations, dense and rotated distributions, large aspect ratios, and imbalances between positive and negative samples. We meticulously review the improvement methods and conduct a detailed analysis of the strengths and weaknesses of these methods. We compile ship information from common optical remote sensing image datasets and compare algorithm performance. Simultaneously, we compare and analyze the feature extraction capabilities of backbones based on CNNs and Transformer, seeking new directions for the development in SDORSIs. Promising prospects are provided to facilitate further research in the future.

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Section: Multi-scale Information-based Methodsmentioning

confidence: 99%

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Zhao,

Wang,

et al. 2024

Remote Sensing

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“…Additionally, prolonged work periods can lead to operator fatigue, posing safety risks. To overcome this challenge, computer vision methods [7,8] applied to ship identification using remote sensing image data have emerged, marking a new era of more precise and efficient ship target detection.…”

Section: Introductionmentioning

confidence: 99%

SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection

Han,

Guo,

Yang

et al. 2024

Drones

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Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%.

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“…One-stage algorithms have attracted more attention because they can reduce both computational costs and model size with minimal accuracy loss. In optical ship detection, YOLO-based detectors have demonstrated that the detection paradigm of "backbone-neck-head" in YOLO has a powerful ability in feature extraction [19,20]. The YOLO series also show its great generality and flexibility in other scenarios, like ship depth estimation [21] and ship instance segmentation [22].…”

Section: Introductionmentioning

confidence: 99%

LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion

Feng,

Lun,

Wang

et al. 2023

Remote Sensing

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In recent years, significant strides have been made in the field of synthetic aperture radar (SAR) ship detection through the application of deep learning techniques. These advanced methods have substantially improved the accuracy of ship detection. Nonetheless, SAR images present distinct challenges, including complex backgrounds, small ship targets, and noise interference, thereby rendering the detectors particularly demanding. In this paper, we introduce LRTransDet, a real-time SAR ship detector. LRTransDet leverages a lightweight vision transformer (ViT) and a multi-scale feature fusion neck to address these challenges effectively. First, our model implements a lightweight backbone that combines convolutional neural networks (CNNs) and transformers, thus enabling it to simultaneously capture both local and global features from input SAR images. Moreover, we boost the model’s efficiency by incorporating the faster weighted feature fusion (Faster-WF2) module and coordinate attention (CA) mechanism within the feature fusion neck. These components optimize computational resources while maintaining the model’s performance. To overcome the challenge of detecting small ship targets in SAR images, we refine the original loss function and use the normalized Wasserstein distance (NWD) metric and the intersection over union (IoU) scheme. This combination improves the detector’s ability to efficiently detect small targets. To prove the performance of our proposed model, we conducted experiments on four challenging datasets (the SSDD, the SAR-Ship Dataset, the HRSID, and the LS-SSDD-v1.0). The results demonstrate that our model surpasses both general object detectors and state-of-the-art SAR ship detectors in terms of detection accuracy (97.8% on the SSDD and 93.9% on the HRSID) and speed (74.6 FPS on the SSDD and 75.8 FPS on the HRSID), all while demanding 3.07 M parameters. Additionally, we conducted a series of ablation experiments to illustrate the impact of the EfficientViT, the Faster-WF2 module, the CA mechanism, and the NWD metric on multi-scale feature fusion and detection performance.

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LMSD-Net: A Lightweight and High-Performance Ship Detection Network for Optical Remote Sensing Images

Cited by 5 publications

References 78 publications

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

Ship Detection with Deep Learning in Optical Remote-Sensing Images: A Survey of Challenges and Advances

SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection

LRTransDet: A Real-Time SAR Ship-Detection Network with Lightweight ViT and Multi-Scale Feature Fusion

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